Methods for Linking Data to Online Resources and Ontologies with Applications to Neurophysiology

TL;DR

This paper introduces the HERD standard and tools for linking datasets to external resources and ontologies, improving data annotation, integration, and reuse in neurophysiology research.

Contribution

It presents the HERD standard and its integration with NWB, enabling metadata annotation without modifying original datasets, thus enhancing data interoperability.

Findings

HERD standard effectively links datasets to external references.

Integration with NWB improves data annotation and reuse.

Tools facilitate metadata management in neurophysiology data.

Abstract

Across many domains, large swaths of digital assets are being stored across distributed data repositories, e.g., the DANDI Archive [8]. The distribution and diversity of these repositories impede researchers from formally defining terminology within experiments, integrating information across datasets, and easily querying, reusing, and analyzing data that follow the FAIR principles [15]. As such, it has become increasingly important to have a standardized method to attach contextual metadata to datasets. Neuroscience is an exemplary use case of this issue due to the complex multimodal nature of experiments. Here, we present the HDMF External Resources Data (HERD) standard and related tools, enabling researchers to annotate new and existing datasets by mapping external references to the data without requiring modification of the original dataset. We integrated HERD closely with Neurodata Without Borders (NWB) [2], a widely used data standard for sharing and storing neurophysiology data. By integrating with NWB, our tools provide neuroscientists with the capability to more easily create and manage neurophysiology data in compliance with controlled sets of terms, enhancing rigor and accuracy of data and facilitating data reuse.